#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Mar 4 15:25:33 2022 @author: tanu """ #%% import os, sys import pandas as pd import numpy as np import pprint as pp from copy import deepcopy from sklearn import linear_model from sklearn import datasets from collections import Counter from sklearn.linear_model import LogisticRegression, LogisticRegressionCV from sklearn.linear_model import RidgeClassifier, RidgeClassifierCV, SGDClassifier, PassiveAggressiveClassifier from sklearn.naive_bayes import BernoulliNB from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier, BaggingClassifier from sklearn.naive_bayes import GaussianNB from sklearn.gaussian_process import GaussianProcessClassifier, kernels from sklearn.gaussian_process.kernels import RBF, DotProduct, Matern, RationalQuadratic, WhiteKernel from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis from sklearn.neural_network import MLPClassifier from sklearn.svm import SVC from xgboost import XGBClassifier from sklearn.naive_bayes import MultinomialNB from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder from sklearn.compose import ColumnTransformer from sklearn.compose import make_column_transformer from sklearn.metrics import make_scorer, confusion_matrix, accuracy_score, balanced_accuracy_score, precision_score, average_precision_score, recall_score from sklearn.metrics import roc_auc_score, roc_curve, f1_score, matthews_corrcoef, jaccard_score, classification_report # added from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, LeaveOneOut, KFold, RepeatedKFold, cross_val_predict from sklearn.model_selection import train_test_split, cross_validate, cross_val_score from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold from sklearn.pipeline import Pipeline, make_pipeline from sklearn.feature_selection import RFE, RFECV import itertools import seaborn as sns import matplotlib.pyplot as plt from statistics import mean, stdev, median, mode from imblearn.over_sampling import RandomOverSampler from imblearn.under_sampling import RandomUnderSampler from imblearn.over_sampling import SMOTE from sklearn.datasets import make_classification from imblearn.combine import SMOTEENN from imblearn.combine import SMOTETomek from imblearn.over_sampling import SMOTENC from imblearn.under_sampling import EditedNearestNeighbours from imblearn.under_sampling import RepeatedEditedNearestNeighbours from sklearn.model_selection import GridSearchCV from sklearn.base import BaseEstimator from sklearn.impute import KNNImputer as KNN import json import argparse import re #%% GLOBALS rs = {'random_state': 42} njobs = {'n_jobs': 10} scoring_fn = ({ 'mcc' : make_scorer(matthews_corrcoef) , 'fscore' : make_scorer(f1_score) , 'precision' : make_scorer(precision_score) , 'recall' : make_scorer(recall_score) , 'accuracy' : make_scorer(accuracy_score) , 'roc_auc' : make_scorer(roc_auc_score) , 'jcc' : make_scorer(jaccard_score) }) skf_cv = StratifiedKFold(n_splits = 10 #, shuffle = False, random_state= None) , shuffle = True,**rs) rskf_cv = RepeatedStratifiedKFold(n_splits = 10 , n_repeats = 3 , **rs) mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)} jacc_score_fn = {'jcc': make_scorer(jaccard_score)} ############################################################################### score_type_ordermapD = { 'mcc' : 1 , 'fscore' : 2 , 'jcc' : 3 , 'precision' : 4 , 'recall' : 5 , 'accuracy' : 6 , 'roc_auc' : 7 , 'TN' : 8 , 'FP' : 9 , 'FN' : 10 , 'TP' : 11 , 'trainingY_neg': 12 , 'trainingY_pos': 13 , 'blindY_neg' : 14 , 'blindY_pos' : 15 , 'fit_time' : 16 , 'score_time' : 17 } scoreCV_mapD = {'test_mcc' : 'MCC' , 'test_fscore' : 'F1' , 'test_precision' : 'Precision' , 'test_recall' : 'Recall' , 'test_accuracy' : 'Accuracy' , 'test_roc_auc' : 'ROC_AUC' , 'test_jcc' : 'JCC' } scoreBT_mapD = {'bts_mcc' : 'MCC' , 'bts_fscore' : 'F1' , 'bts_precision' : 'Precision' , 'bts_recall' : 'Recall' , 'bts_accuracy' : 'Accuracy' , 'bts_roc_auc' : 'ROC_AUC' , 'bts_jcc' : 'JCC' } #%%############################################################################ ############################ # MultModelsCl() # Run Multiple Classifiers ############################ # Multiple Classification - Model Pipeline def MultModelsCl(input_df, target, skf_cv , blind_test_df , blind_test_target , tts_split_type , resampling_type = 'none' # default , add_cm = True # adds confusion matrix based on cross_val_predict , add_yn = True # adds target var class numbers , var_type = ['numerical', 'categorical','mixed'] , return_formatted_output = True): ''' @ param input_df: input features @ type: df with input features WITHOUT the target variable @param target: target (or output) feature @type: df or np.array or Series @param skv_cv: stratifiedK fold int or object to allow shuffle and random state to pass @type: int or StratifiedKfold() @var_type: numerical, categorical and mixed to determine what col_transform to apply (MinMaxScalar and/or one-ho t encoder) @type: list returns Dict containing multiple classification scores for each model and mean of each Stratified Kfold including training ''' #====================================================== # Determine categorical and numerical features #====================================================== numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns numerical_ix categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns categorical_ix #====================================================== # Determine preprocessing steps ~ var_type #====================================================== if var_type == 'numerical': t = [('num', MinMaxScaler(), numerical_ix)] if var_type == 'categorical': t = [('cat', OneHotEncoder(), categorical_ix)] if var_type == 'mixed': t = [('num', MinMaxScaler(), numerical_ix) , ('cat', OneHotEncoder(), categorical_ix) ] col_transform = ColumnTransformer(transformers = t , remainder='passthrough') #====================================================== # Specify multiple Classification Models #====================================================== models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) ) , ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True) ) , ('Decision Tree' , DecisionTreeClassifier(**rs) ) , ('Extra Tree' , ExtraTreeClassifier(**rs) ) , ('Extra Trees' , ExtraTreesClassifier(**rs) ) , ('Gradient Boosting' , GradientBoostingClassifier(**rs) ) , ('Gaussian NB' , GaussianNB() ) , ('Gaussian Process' , GaussianProcessClassifier(**rs) ) , ('K-Nearest Neighbors' , KNeighborsClassifier() ) , ('LDA' , LinearDiscriminantAnalysis() ) , ('Logistic Regression' , LogisticRegression(**rs) ) , ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs)) , ('MLP' , MLPClassifier(max_iter = 500, **rs) ) , ('Multinomial' , MultinomialNB() ) , ('Naive Bayes' , BernoulliNB() ) , ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) ) , ('QDA' , QuadraticDiscriminantAnalysis() ) , ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000 ) ) , ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5 , n_estimators = 1000 , bootstrap = True , oob_score = True , **njobs , **rs , max_features = 'auto') ) , ('Ridge Classifier' , RidgeClassifier(**rs) ) , ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) ) , ('SVC' , SVC(**rs) ) , ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) ) , ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False) ) ] mm_skf_scoresD = {} print('\n==============================================================\n' , '\nRunning several classification models (n):', len(models) ,'\nList of models:') for m in models: print(m) print('\n================================================================\n') index = 1 for model_name, model_fn in models: print('\nRunning classifier:', index , '\nModel_name:' , model_name , '\nModel func:' , model_fn) index = index+1 model_pipeline = Pipeline([ ('prep' , col_transform) , ('model' , model_fn)]) print('\nRunning model pipeline:', model_pipeline) skf_cv_modD = cross_validate(model_pipeline , input_df , target , cv = skf_cv , scoring = scoring_fn , return_train_score = True) ####################################################################### #====================================================== # Option: Add confusion matrix from cross_val_predict # Understand and USE with caution # cross_val_score, cross_val_predict, "Passing these predictions into an evaluation metric may not be a valid way to measure generalization performance. Results can differ from cross_validate and cross_val_score unless all tests sets have equal size and the metric decomposes over samples." # https://stackoverflow.com/questions/65645125/producing-a-confusion-matrix-with-cross-validate #====================================================== if add_cm: #----------------------------------------------------------- # Initialise dict of Confusion Matrix (cm) #----------------------------------------------------------- cmD = {} # Calculate cm y_pred = cross_val_predict(model_pipeline, input_df, target, cv = skf_cv, **njobs) #_tn, _fp, _fn, _tp = confusion_matrix(y_pred, y).ravel() # internally tn, fp, fn, tp = confusion_matrix(y_pred, target).ravel() # Build dict cmD = {'TN' : tn , 'FP': fp , 'FN': fn , 'TP': tp} #--------------------------------- # Update cv dict with cmD and tbtD #---------------------------------- skf_cv_modD.update(cmD) else: skf_cv_modD = skf_cv_modD ####################################################################### #============================================= # Option: Add targety numbers for data #============================================= if add_yn: #----------------------------------------------------------- # Initialise dict of target numbers: training and blind (tbt) #----------------------------------------------------------- tbtD = {} # training y tyn = Counter(target) tyn_neg = tyn[0] tyn_pos = tyn[1] # blind test y btyn = Counter(blind_test_target) btyn_neg = btyn[0] btyn_pos = btyn[1] # Build dict tbtD = {'n_trainingY_neg' : tyn_neg , 'n_trainingY_pos' : tyn_pos , 'n_blindY_neg' : btyn_neg , 'n_blindY_pos' : btyn_pos} #--------------------------------- # Update cv dict with cmD and tbtD #---------------------------------- skf_cv_modD.update(tbtD) else: skf_cv_modD = skf_cv_modD ####################################################################### #============================== # Extract mean values for CV #============================== mm_skf_scoresD[model_name] = {} for key, value in skf_cv_modD.items(): print('\nkey:', key, '\nvalue:', value) print('\nmean value:', np.mean(value)) mm_skf_scoresD[model_name][key] = round(np.mean(value),2) #return(mm_skf_scoresD) #%% #========================= # Blind test: BTS results #========================= # Build the final results with all scores for the model #bts_predict = gscv_fs.predict(blind_test_df) model_pipeline.fit(input_df, target) bts_predict = model_pipeline.predict(blind_test_df) bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2) print('\nMCC on Blind test:' , bts_mcc_score) print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2)) # Diff b/w train and bts test scores # train_test_diff_MCC = cvtrain_mcc - bts_mcc_score # print('\nDiff b/w train and blind test score (MCC):', train_test_diff) mm_skf_scoresD[model_name]['bts_mcc'] = bts_mcc_score mm_skf_scoresD[model_name]['bts_fscore'] = round(f1_score(blind_test_target, bts_predict),2) mm_skf_scoresD[model_name]['bts_precision'] = round(precision_score(blind_test_target, bts_predict),2) mm_skf_scoresD[model_name]['bts_recall'] = round(recall_score(blind_test_target, bts_predict),2) mm_skf_scoresD[model_name]['bts_accuracy'] = round(accuracy_score(blind_test_target, bts_predict),2) mm_skf_scoresD[model_name]['bts_roc_auc'] = round(roc_auc_score(blind_test_target, bts_predict),2) mm_skf_scoresD[model_name]['bts_jcc'] = round(jaccard_score(blind_test_target, bts_predict),2) #mm_skf_scoresD[model_name]['diff_mcc'] = train_test_diff_MCC #return(mm_skf_scoresD) #%% # ADD more info: meta data related to input and blind and resampling # target numbers: training yc1 = Counter(target) yc1_ratio = yc1[0]/yc1[1] # target numbers: test yc2 = Counter(blind_test_target) yc2_ratio = yc2[0]/yc2[1] mm_skf_scoresD[model_name]['resampling'] = resampling_type mm_skf_scoresD[model_name]['n_training_size'] = len(input_df) mm_skf_scoresD[model_name]['n_trainingY_ratio'] = round(yc1_ratio, 2) mm_skf_scoresD[model_name]['n_test_size'] = len(blind_test_df) mm_skf_scoresD[model_name]['n_testY_ratio'] = round(yc2_ratio,2) mm_skf_scoresD[model_name]['n_features'] = len(input_df.columns) mm_skf_scoresD[model_name]['tts_split'] = tts_split_type #return(mm_skf_scoresD) #============================ # Process the dict to have WF #============================ if return_formatted_output: CV_BT_metaDF = ProcessMultModelsCl(mm_skf_scoresD) return(CV_BT_metaDF) else: return(mm_skf_scoresD) #%% Process output function ################################################### ############################ # ProcessMultModelsCl() ############################ #Processes the dict from above if use_formatted_output = True def ProcessMultModelsCl(inputD = {}): scoresDF = pd.DataFrame(inputD) #------------------------ # Extracting split_name #----------------------- tts_split_nameL = [] for k,v in inputD.items(): tts_split_nameL = tts_split_nameL + [v['tts_split']] if len(set(tts_split_nameL)) == 1: tts_split_name = str(list(set(tts_split_nameL))[0]) print('\nExtracting tts_split_name:', tts_split_name) #------------------------ # WF: only CV and BTS #----------------------- scoresDFT = scoresDF.T scoresDF_CV = scoresDFT.filter(regex='^test_.*$', axis = 1); scoresDF_CV.columns # map colnames for consistency to allow concatenting scoresDF_CV.columns = scoresDF_CV.columns.map(scoreCV_mapD); scoresDF_CV.columns scoresDF_CV['source_data'] = 'CV' scoresDF_BT = scoresDFT.filter(regex='^bts_.*$', axis = 1); scoresDF_BT.columns # map colnames for consistency to allow concatenting scoresDF_BT.columns = scoresDF_BT.columns.map(scoreBT_mapD); scoresDF_BT.columns scoresDF_BT['source_data'] = 'BT' # dfs_combine_wf = [baseline_BT, smnc_BT, ros_BT, rus_BT, rouC_BT, # baseline_CV, smnc_CV, ros_CV, rus_CV, rouC_CV] #baseline_all = baseline_all_scores.filter(regex = 'bts_.*|test_.*|.*_time|TN|FP|FN|TP|.*_neg|.*_pos', axis = 0) #metaDF = scoresDFT.filter(regex='training_size|blind_test_size|_time|TN|FP|FN|TP|.*_neg|.*_pos|resampling', axis = 1); scoresDF_BT.columns #metaDF = scoresDFT.filter(regex='n_.*$|_time|TN|FP|FN|TP|.*_neg|.*_pos|resampling|tts.*', axis = 1); metaDF.columns metaDF = scoresDFT.filter(regex='^(?!test_.*$|bts_.*$|train_.*$).*'); metaDF.columns print('\nTotal cols in each df:' , '\nCV df:', len(scoresDF_CV.columns) , '\nBT_df:', len(scoresDF_BT.columns) , '\nmetaDF:', len(metaDF.columns)) if len(scoresDF_CV.columns) == len(scoresDF_BT.columns): print('\nFirst proceeding to rowbind CV and BT dfs:') expected_ncols_out = len(scoresDF_BT.columns) + len(metaDF.columns) print('\nFinal output should have:', expected_ncols_out, 'columns' ) #----------------- # Combine WF #----------------- dfs_combine_wf = [scoresDF_CV, scoresDF_BT] print('\nCombinig', len(dfs_combine_wf), 'using pd.concat by row ~ rowbind' , '\nChecking Dims of df to combine:' , '\nDim of CV:', scoresDF_CV.shape , '\nDim of BT:', scoresDF_BT.shape) #print(scoresDF_CV) #print(scoresDF_BT) dfs_nrows_wf = [] for df in dfs_combine_wf: dfs_nrows_wf = dfs_nrows_wf + [len(df)] dfs_nrows_wf = max(dfs_nrows_wf) dfs_ncols_wf = [] for df in dfs_combine_wf: dfs_ncols_wf = dfs_ncols_wf + [len(df.columns)] dfs_ncols_wf = max(dfs_ncols_wf) print(dfs_ncols_wf) expected_nrows_wf = len(dfs_combine_wf) * dfs_nrows_wf expected_ncols_wf = dfs_ncols_wf common_cols_wf = list(set.intersection(*(set(df.columns) for df in dfs_combine_wf))) print('\nNumber of Common columns:', dfs_ncols_wf , '\nThese are:', common_cols_wf) if len(common_cols_wf) == dfs_ncols_wf : combined_baseline_wf = pd.concat([df[common_cols_wf] for df in dfs_combine_wf], ignore_index=False) print('\nConcatenating dfs with different resampling methods [WF]:' , '\nSplit type:', tts_split_name , '\nNo. of dfs combining:', len(dfs_combine_wf)) #print('\n================================================^^^^^^^^^^^^') if len(combined_baseline_wf) == expected_nrows_wf and len(combined_baseline_wf.columns) == expected_ncols_wf: #print('\n================================================^^^^^^^^^^^^') print('\nPASS:', len(dfs_combine_wf), 'dfs successfully combined' , '\nnrows in combined_df_wf:', len(combined_baseline_wf) , '\nncols in combined_df_wf:', len(combined_baseline_wf.columns)) else: print('\nFAIL: concatenating failed' , '\nExpected nrows:', expected_nrows_wf , '\nGot:', len(combined_baseline_wf) , '\nExpected ncols:', expected_ncols_wf , '\nGot:', len(combined_baseline_wf.columns)) sys.exit('\nFIRST IF FAILS') else: print('\nConcatenting dfs not possible [WF],check numbers ') #------------------------------------- # Combine WF+Metadata: Final output #------------------------------------- # checking indices for the dfs to combine: c1L = list(set(combined_baseline_wf.index)) c2L = list(metaDF.index) #if set(c1L) == set(c2L): if set(c1L) == set(c2L) and all(x in c2L for x in c1L) and all(x in c1L for x in c2L): print('\nPASS: proceeding to merge metadata with CV and BT dfs') combDF = pd.merge(combined_baseline_wf, metaDF, left_index = True, right_index = True) else: sys.exit('\nFAIL: Could not merge metadata with CV and BT dfs') if len(combDF.columns) == expected_ncols_out: print('\nPASS: Combined df has expected ncols') else: sys.exit('\nFAIL: Length mismatch for combined_df') print('\nAdding column: Model_name') combDF['Model_name'] = combDF.index print('\n=========================================================' , '\nSUCCESS: Ran multiple classifiers' , '\n=======================================================') #resampling_methods_wf = combined_baseline_wf[['resampling']] #resampling_methods_wf = resampling_methods_wf.drop_duplicates() #, '\n', resampling_methods_wf) return combDF ############################################################################### #%% Feature selection function ################################################ ############################ # fsgs_rfecv() ############################ # Run FS using some classifier models # def fsgs_rfecv(input_df , target , param_gridLd = [{'fs__min_features_to_select' : [1]}] , blind_test_df = pd.DataFrame() , blind_test_target = pd.Series(dtype = 'int64') , estimator = LogisticRegression(**rs) # placeholder , use_fs = False # uses estimator as the RFECV parameter for fs. Set to TRUE if you want to supply custom_fs as shown below , custom_fs = RFECV(DecisionTreeClassifier(**rs) , cv = skf_cv, scoring = 'matthews_corrcoef') , cv_method = skf_cv , var_type = ['numerical', 'categorical' , 'mixed'] , verbose = 3 ): ''' returns Dict containing results from FS and hyperparam tuning for a given estiamtor >>> ADD MORE <<< optimised/selected based on mcc ''' ########################################################################### #================================================ # Determine categorical and numerical features #================================================ numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns numerical_ix categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns categorical_ix #================================================ # Determine preprocessing steps ~ var_type #================================================ if var_type == 'numerical': t = [('num', MinMaxScaler(), numerical_ix)] if var_type == 'categorical': t = [('cat', OneHotEncoder(), categorical_ix)] if var_type == 'mixed': t = [('cat', OneHotEncoder(), categorical_ix) , ('num', MinMaxScaler(), numerical_ix)] col_transform = ColumnTransformer(transformers = t , remainder='passthrough') ########################################################################### #================================================== # Create var_type ~ column names # using one hot encoder with RFECV means # the names internally are lost. Hence # fit col_transformeer to my input_df and get # all the column names out and stored in a var # to allow the 'selected features' to be subsetted # from the numpy boolean array #================================================= col_transform.fit(input_df) col_transform.get_feature_names_out() var_type_colnames = col_transform.get_feature_names_out() var_type_colnames = pd.Index(var_type_colnames) if var_type == 'mixed': print('\nVariable type is:', var_type , '\nNo. of columns in input_df:', len(input_df.columns) , '\nNo. of columns post one hot encoder:', len(var_type_colnames)) else: print('\nNo. of columns in input_df:', len(input_df.columns)) #================================== # Build FS with supplied estimator #================================== if use_fs: fs = custom_fs else: fs = RFECV(estimator, cv = skf_cv, scoring = 'matthews_corrcoef') #================================== # Build basic param grid #================================== # param_gridD = [ # {'fs__min_features_to_select' : [1] # }] ############################################################################ # Create Pipeline object pipe = Pipeline([ ('pre', col_transform), ('fs', fs), ('clf', estimator)]) ############################################################################ # Define GridSearchCV gscv_fs = GridSearchCV(pipe #, param_gridLd = param_gridD , param_gridLd , cv = cv_method , scoring = scoring_fn , refit = 'mcc' , verbose = 3 , return_train_score = True , **njobs) gscv_fs.fit(input_df, target) ########################################################################### # Get best param and scores out gscv_fs.best_params_ gscv_fs.best_score_ # Training best score corresponds to the max of the mean_test train_bscore = round(gscv_fs.best_score_, 2); train_bscore print('\nTraining best score (MCC):', train_bscore) gscv_fs.cv_results_['mean_test_mcc'] round(gscv_fs.cv_results_['mean_test_mcc'].max(),2) round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2) check_train_score = [round(gscv_fs.cv_results_['mean_test_mcc'].max(),2) , round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)] check_train_score = np.nanmax(check_train_score) # Training results gscv_tr_resD = gscv_fs.cv_results_ mod_refit_param = gscv_fs.refit # sanity check if train_bscore == check_train_score: print('\nVerified training score (MCC):', train_bscore ) else: sys.exit('\nTraining score could not be internatlly verified. Please check training results dict') #------------------------- # Dict of CV results #------------------------- cv_allD = gscv_fs.cv_results_ cvdf0 = pd.DataFrame(cv_allD) cvdf = cvdf0.filter(regex='mean_test', axis = 1) cvdfT = cvdf.T cvdfT.columns = ['cv_score'] cvdfTr = cvdfT.loc[:,'cv_score'].round(decimals = 2) # round values cvD = cvdfTr.to_dict() print('\n CV results dict generated for:', len(scoring_fn), 'scores' , '\nThese are:', scoring_fn.keys()) #------------------------- # Blind test: REAL check! #------------------------- #tp = gscv_fs.predict(X_bts) tp = gscv_fs.predict(blind_test_df) print('\nMCC on Blind test:' , round(matthews_corrcoef(blind_test_target, tp),2)) print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, tp),2)) #================= # info extraction #================= # gives input vals?? gscv_fs._check_n_features # gives gscv params used gscv_fs._get_param_names() # gives ?? gscv_fs.best_estimator_ gscv_fs.best_params_ # gives best estimator params as a dict gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter gscv_fs.best_estimator_.named_steps['fs'].get_support() gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean() gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max() #gscv_fs.best_estimator_.named_steps['fs'].grid_scores_ estimator_mask = gscv_fs.best_estimator_.named_steps['fs'].get_support() ############################################################################ #============ # FS results #============ # Now get the features out #-------------- # All features #-------------- all_features = gscv_fs.feature_names_in_ n_all_features = gscv_fs.n_features_in_ #all_features = gsfit.feature_names_in_ #-------------- # Selected features by the classifier # Important to have var_type_colnames here #---------------- #sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()] 3 only for numerical df sel_features = var_type_colnames[gscv_fs.best_estimator_.named_steps['fs'].get_support()] n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_ #-------------- # Get model name #-------------- model_name = gscv_fs.best_estimator_.named_steps['clf'] b_model_params = gscv_fs.best_params_ print('\n========================================' , '\nRunning model:' , '\nModel name:', model_name , '\n===============================================' , '\nRunning feature selection with RFECV for model' , '\nTotal no. of features in model:', len(all_features) , '\nThese are:\n', all_features, '\n\n' , '\nNo of features for best model: ', n_sf , '\nThese are:', sel_features, '\n\n' , '\nBest Model hyperparams:', b_model_params ) ########################################################################### ############################## OUTPUT ##################################### ########################################################################### #========================= # Blind test: BTS results #========================= # Build the final results with all scores for a feature selected model #bts_predict = gscv_fs.predict(X_bts) bts_predict = gscv_fs.predict(blind_test_df) print('\nMCC on Blind test:' , round(matthews_corrcoef(blind_test_target, bts_predict),2)) print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2)) bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2) # Diff b/w train and bts test scores train_test_diff = train_bscore - bts_mcc_score print('\nDiff b/w train and blind test score (MCC):', train_test_diff) lr_btsD ={} #lr_btsD['bts_mcc'] = bts_mcc_score lr_btsD['bts_fscore'] = round(f1_score(blind_test_target, bts_predict),2) lr_btsD['bts_precision'] = round(precision_score(blind_test_target, bts_predict),2) lr_btsD['bts_recall'] = round(recall_score(blind_test_target, bts_predict),2) lr_btsD['bts_accuracy'] = round(accuracy_score(blind_test_target, bts_predict),2) lr_btsD['bts_roc_auc'] = round(roc_auc_score(blind_test_target, bts_predict),2) lr_btsD['bts_jcc'] = round(jaccard_score(blind_test_target, bts_predict),2) lr_btsD #=========================== # Add FS related model info #=========================== model_namef = str(model_name) # FIXME: doesn't tell you which it has chosen fs_methodf = str(gscv_fs.best_estimator_.named_steps['fs']) all_featuresL = list(all_features) fs_res_arrayf = str(list( gscv_fs.best_estimator_.named_steps['fs'].get_support())) fs_res_array_rankf = str(list( gscv_fs.best_estimator_.named_steps['fs'].ranking_)) sel_featuresf = list(sel_features) n_sf = int(n_sf) output_modelD = {'model_name': model_namef , 'model_refit_param': mod_refit_param , 'Best_model_params': b_model_params , 'n_all_features': n_all_features , 'fs_method': fs_methodf , 'fs_res_array': fs_res_arrayf , 'fs_res_array_rank': fs_res_array_rankf , 'all_feature_names': all_featuresL , 'n_sel_features': n_sf , 'sel_features_names': sel_featuresf} #output_modelD #======================================== # Update output_modelD with bts_results #======================================== output_modelD.update(lr_btsD) output_modelD output_modelD['train_score (MCC)'] = train_bscore output_modelD['bts_mcc'] = bts_mcc_score output_modelD['train_bts_diff'] = round(train_test_diff,2) print(output_modelD) nlen = len(output_modelD) #======================================== # Update output_modelD with cv_results #======================================== output_modelD.update(cvD) if (len(output_modelD) == nlen + len(cvD)): print('\nFS run complete for model:', estimator , '\nFS using:', fs , '\nOutput dict size:', len(output_modelD)) return(output_modelD) else: sys.exit('\nFAIL:numbers mismatch output dict length not as expected. Please check')